from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel
@require_tf
class     lowerCAmelCase_     :
							UpperCAmelCase				   =     BlenderbotConfig
							UpperCAmelCase				   =     {}
							UpperCAmelCase				   =     "gelu"







							def __init__(   self      :     List[str] ,			_A      :     List[str] ,			_A      :     Union[str, Any]=13 ,			_A      :     List[Any]=7 ,			_A      :     Any=True ,			_A      :     Tuple=False ,			_A      :     str=99 ,			_A      :     int=32 ,			_A      :     Optional[Any]=2 ,			_A      :     Optional[int]=4 ,			_A      :     Optional[Any]=37 ,			_A      :     Any=0.1 ,			_A      :     int=0.1 ,			_A      :     Union[str, Any]=20 ,			_A      :     List[str]=2 ,			_A      :     Tuple=1 ,			_A      :     Any=0 ,			):
												_UpperCamelCase			     =     parent
												_UpperCamelCase			     =     batch_size
												_UpperCamelCase			     =     seq_length
												_UpperCamelCase			     =     is_training
												_UpperCamelCase			     =     use_labels
												_UpperCamelCase			     =     vocab_size
												_UpperCamelCase			     =     hidden_size
												_UpperCamelCase			     =     num_hidden_layers
												_UpperCamelCase			     =     num_attention_heads
												_UpperCamelCase			     =     intermediate_size

												_UpperCamelCase			     =     hidden_dropout_prob
												_UpperCamelCase			     =     attention_probs_dropout_prob
												_UpperCamelCase			     =     max_position_embeddings
												_UpperCamelCase			     =     eos_token_id
												_UpperCamelCase			     =     pad_token_id
												_UpperCamelCase			     =     bos_token_id







							def  UpperCamelCase_						(   self      :     str		):
												_UpperCamelCase			     =     ids_tensor([self.batch_size, self.seq_length - 1] ,			self.vocab_size		)
												_UpperCamelCase			     =     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		) ,			1		)
												_UpperCamelCase			     =     tf.concat([input_ids, eos_tensor] ,			axis=1		)

												_UpperCamelCase			     =     ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size		)

												_UpperCamelCase			     =     self.config_cls(
												    vocab_size=self.vocab_size ,			d_model=self.hidden_size ,			encoder_layers=self.num_hidden_layers ,			decoder_layers=self.num_hidden_layers ,			encoder_attention_heads=self.num_attention_heads ,			decoder_attention_heads=self.num_attention_heads ,			encoder_ffn_dim=self.intermediate_size ,			decoder_ffn_dim=self.intermediate_size ,			dropout=self.hidden_dropout_prob ,			attention_dropout=self.attention_probs_dropout_prob ,			max_position_embeddings=self.max_position_embeddings ,			eos_token_ids=[2] ,			bos_token_id=self.bos_token_id ,			pad_token_id=self.pad_token_id ,			decoder_start_token_id=self.pad_token_id ,			**self.config_updates ,			)
												_UpperCamelCase			     =     prepare_blenderbot_inputs_dict(_A ,			_A ,			_A		)
												return config, inputs_dict







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     Union[str, Any] ,			_A      :     Union[str, Any]		):
												_UpperCamelCase			     =     TFBlenderbotModel(config=_A		).get_decoder()
												_UpperCamelCase			     =     inputs_dict['''input_ids''']

												_UpperCamelCase			     =     input_ids[:1, :]
												_UpperCamelCase			     =     inputs_dict['''attention_mask'''][:1, :]
												_UpperCamelCase			     =     inputs_dict['''head_mask''']
												_UpperCamelCase			     =     1

												# first forward pass
												_UpperCamelCase			     =     model(_A ,			attention_mask=_A ,			head_mask=_A ,			use_cache=_A		)

												_UpperCamelCase     ,    _UpperCamelCase			     =     outputs.to_tuple()

												# create hypothetical next token and extent to next_input_ids
												_UpperCamelCase			     =     ids_tensor((self.batch_size, 3) ,			config.vocab_size		)
												_UpperCamelCase			     =     tf.cast(ids_tensor((self.batch_size, 3) ,			2		) ,			tf.inta		)

												# append to next input_ids and
												_UpperCamelCase			     =     tf.concat([input_ids, next_tokens] ,			axis=-1		)
												_UpperCamelCase			     =     tf.concat([attention_mask, next_attn_mask] ,			axis=-1		)

												_UpperCamelCase			     =     model(_A ,			attention_mask=_A		)[0]
												_UpperCamelCase			     =     model(_A ,			attention_mask=_A ,			past_key_values=_A		)[0]

												self.parent.assertEqual(next_tokens.shape[1] ,			output_from_past.shape[1]		)

												# select random slice
												_UpperCamelCase			     =     int(ids_tensor((1,) ,			output_from_past.shape[-1]		)		)
												_UpperCamelCase			     =     output_from_no_past[:, -3:, random_slice_idx]
												_UpperCamelCase			     =     output_from_past[:, :, random_slice_idx]

												# test that outputs are equal for slice
												tf.debugging.assert_near(_A ,			_A ,			rtol=1e-3		)




def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case=None  ,     __snake_case=None  ,     __snake_case=None  ,     __snake_case=None  ,     __snake_case=None  ,     ):
					if attention_mask is None:
										_UpperCamelCase			     =     tf.cast(tf.math.not_equal(__snake_case  ,     config.pad_token_id						)  ,     tf.inta						)
					if decoder_attention_mask is None:
										_UpperCamelCase			     =     tf.concat(
										    [
										        tf.ones(decoder_input_ids[:, :1].shape  ,     dtype=tf.inta						),
										        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]  ,     config.pad_token_id						)  ,     tf.inta						),
										    ]  ,     axis=-1  ,     )
					if head_mask is None:
										_UpperCamelCase			     =     tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
					if decoder_head_mask is None:
										_UpperCamelCase			     =     tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
					if cross_attn_head_mask is None:
										_UpperCamelCase			     =     tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
					return {
					    "input_ids": input_ids,
					    "decoder_input_ids": decoder_input_ids,
					    "attention_mask": attention_mask,
					    "decoder_attention_mask": decoder_attention_mask,
					    "head_mask": head_mask,
					    "decoder_head_mask": decoder_head_mask,
					    "cross_attn_head_mask": cross_attn_head_mask,
					}
@require_tf
class     lowerCAmelCase_     (       __lowercase,  __lowercase,  unittest.TestCase ):
							UpperCAmelCase				   =     (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
							UpperCAmelCase				   =     (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
							UpperCAmelCase				   =     (
							    {
							        "conversational": TFBlenderbotForConditionalGeneration,
							        "feature-extraction": TFBlenderbotModel,
							        "summarization": TFBlenderbotForConditionalGeneration,
							        "text2text-generation": TFBlenderbotForConditionalGeneration,
							        "translation": TFBlenderbotForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							UpperCAmelCase				   =     True
							UpperCAmelCase				   =     False
							UpperCAmelCase				   =     False







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												_UpperCamelCase			     =     TFBlenderbotModelTester(self		)
												_UpperCamelCase			     =     ConfigTester(self ,			config_class=_A		)







							def  UpperCamelCase_						(   self      :     Dict		):
												self.config_tester.run_common_tests()







							def  UpperCamelCase_						(   self      :     Tuple		):
												_UpperCamelCase			     =     self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_decoder_model_past_large_inputs(*_A		)




@require_tokenizers
@require_tf
class     lowerCAmelCase_     (       unittest.TestCase ):
							UpperCAmelCase				   =     ["My friends are cool but they eat too many carbs."]
							UpperCAmelCase				   =     "facebook/blenderbot-400M-distill"







							@cached_property
							def  UpperCamelCase_						(   self      :     List[str]		):
												return BlenderbotTokenizer.from_pretrained(self.model_name		)







							@cached_property
							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												_UpperCamelCase			     =     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
												return model







							@slow
							def  UpperCamelCase_						(   self      :     Optional[int]		):
												_UpperCamelCase			     =     self.tokenizer(self.src_text ,			return_tensors='''tf'''		)
												_UpperCamelCase			     =     self.model.generate(
												    model_inputs.input_ids ,			)
												_UpperCamelCase			     =     self.tokenizer.batch_decode(generated_ids.numpy() ,			skip_special_tokens=_A		)[0]
												assert (
												    generated_words
												    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
												)


"""simple docstring"""






import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase        =				logging.get_logger(__name__)

UpperCAmelCase        =				{
    """nvidia/segformer-b0-finetuned-ade-512-512""": (
        """https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"""
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}



class 			lowercase					( lowercase__		):
			lowercase        =     '''segformer'''






			def __init__(self  :	str      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=3      ,SCREAMING_SNAKE_CASE_  :	List[str]=4      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=[2, 2, 2, 2]      ,SCREAMING_SNAKE_CASE_  :	List[str]=[8, 4, 2, 1]      ,SCREAMING_SNAKE_CASE_  :	int=[32, 64, 160, 256]      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=[7, 3, 3, 3]      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=[4, 2, 2, 2]      ,SCREAMING_SNAKE_CASE_  :	List[Any]=[1, 2, 5, 8]      ,SCREAMING_SNAKE_CASE_  :	Dict=[4, 4, 4, 4]      ,SCREAMING_SNAKE_CASE_  :	Optional[int]="gelu"      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=0.0      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=0.0      ,SCREAMING_SNAKE_CASE_  :	List[str]=0.1      ,SCREAMING_SNAKE_CASE_  :	List[Any]=0.02      ,SCREAMING_SNAKE_CASE_  :	str=0.1      ,SCREAMING_SNAKE_CASE_  :	Dict=1e-6      ,SCREAMING_SNAKE_CASE_  :	List[str]=256      ,SCREAMING_SNAKE_CASE_  :	List[str]=255      ,**SCREAMING_SNAKE_CASE_  :	Tuple      ,)       ->	int:





				"""simple docstring"""


				super().__init__(**SCREAMING_SNAKE_CASE_			)

				if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
					warnings.warn(
					    '''Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'''
					    ''' removed, as the behaviour will default to that of reshape_last_stage = True.'''      ,SCREAMING_SNAKE_CASE_      ,)

				lowerCAmelCase												=  num_channels
				lowerCAmelCase												=  num_encoder_blocks
				lowerCAmelCase												=  depths
				lowerCAmelCase												=  sr_ratios
				lowerCAmelCase												=  hidden_sizes
				lowerCAmelCase												=  patch_sizes
				lowerCAmelCase												=  strides
				lowerCAmelCase												=  mlp_ratios
				lowerCAmelCase												=  num_attention_heads
				lowerCAmelCase												=  hidden_act
				lowerCAmelCase												=  hidden_dropout_prob
				lowerCAmelCase												=  attention_probs_dropout_prob
				lowerCAmelCase												=  classifier_dropout_prob
				lowerCAmelCase												=  initializer_range
				lowerCAmelCase												=  drop_path_rate
				lowerCAmelCase												=  layer_norm_eps
				lowerCAmelCase												=  decoder_hidden_size
				lowerCAmelCase												=  kwargs.get('''reshape_last_stage'''      ,SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  semantic_loss_ignore_index





class 			lowercase					( lowercase__		):
			lowercase        =     version.parse('''1.11'''		)






			@property
			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	Mapping[str, Mapping[int, str]]:





				"""simple docstring"""


				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				    ]			)






			@property
			def 					UpperCAmelCase							(self  :	str			)       ->	float:





				"""simple docstring"""


				return 1e-4






			@property
			def 					UpperCAmelCase							(self  :	Dict			)       ->	int:





				"""simple docstring"""


				return 12


